Transcript Data Representation

Chapter 2
Data Representation
in Computer
Systems
Chapter 2 Objectives
• Understand the fundamentals of numerical data
representation and manipulation in digital
computers.
• Master the skill of converting between various
radix systems.
• Understand how errors can occur in computations
because of overflow and truncation.
2
Chapter 2 Objectives
• Understand the fundamental concepts of floatingpoint representation.
• Gain familiarity with the most popular character
codes.
• Understand the concepts of error detecting and
correcting codes.
3
2.1 Introduction
• A bit is the most basic unit of information in a
computer.
– It is a state of “on” or “off” in a digital circuit.
– Sometimes these states are “high” or “low” voltage
instead of “on” or “off..”
• A byte is a group of eight bits.
– A byte is the smallest possible addressable unit of
computer storage.
– The term, “addressable,” means that a particular byte can
be retrieved according to its location in memory.
4
2.1 Introduction
• A word is a contiguous group of bytes.
– Words can be any number of bits or bytes.
– Word sizes of 16, 32, or 64 bits are most common.
– In a word-addressable system, a word is the smallest
addressable unit of storage.
• A group of four bits is called a nibble.
– Bytes, therefore, consist of two nibbles: a “high-order
nibble,” and a “low-order” nibble.
5
2.2 Positional Numbering Systems
• Bytes store numbers using the position of each
bit to represent a power of 2.
– The binary system is also called the base-2 system.
– Our decimal system is the base-10 system. It uses
powers of 10 for each position in a number.
– Any integer quantity can be represented exactly using
any base (or radix).
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2.2 Positional Numbering Systems
• The decimal number 947 in powers of 10 is:
9  10 2 + 4  10 1 + 7  10 0
• The decimal number 5836.47 in powers of 10 is:
5  10 3 + 8  10 2 + 3  10 1 + 6  10 0
+ 4  10 -1 + 7  10 -2
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2.2 Positional Numbering Systems
• The binary number 11001 in powers of 2 is:
1  24+ 1  23 + 0  22 + 0  21 + 1  20
= 16
+
8
+ 0
+
0
+ 1 = 25
• When the radix of a number is something other
than 10, the base is denoted by a subscript.
– Sometimes, the subscript 10 is added for emphasis:
110012 = 2510
8
2.3 Converting Between Bases
• Because binary numbers are the basis for all data
representation in digital computer systems, it is
important that you become proficient with this radix
system.
• Your knowledge of the binary numbering system
will enable you to understand the operation of all
computer components as well as the design of
instruction set architectures.
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2.3 Converting Between Bases
• In an earlier slide, we said that every integer value
can be represented exactly using any radix
system.
• There are two methods for radix conversion: the
subtraction method and the division remainder
method.
• The subtraction method is more intuitive, but
cumbersome. It does, however reinforce the ideas
behind radix mathematics.
10
2.3 Converting Between Bases
• Suppose we want to
convert the decimal
number 190 to base 3.
– We know that 3 5 = 243 so
our result will be less than
six digits wide. The largest
power of 3 that we need is
therefore 3 4 = 81, and
81  2 = 162.
– Write down the 2 and
subtract 162 from 190,
giving 28.
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2.3 Converting Between Bases
• Converting 190 to base 3...
– The next power of 3 is
3 3 = 27. We’ll need one
of these, so we subtract 27
and write down the numeral
1 in our result.
– The next power of 3, 3 2 =
9, is too large, but we have
to assign a placeholder of
zero and carry down the 1.
12
2.3 Converting Between Bases
• Converting 190 to base 3...
– 3 1 = 3 is again too large,
so we assign a zero
placeholder.
– The last power of 3, 3 0 =
1, is our last choice, and it
gives us a difference of
zero.
– Our result, reading from
top to bottom is:
19010 = 210013
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2.3 Converting Between Bases
• Another method of converting integers from
decimal to some other radix uses division.
• This method is mechanical and easy.
• It employs the idea that successive division by a
base is equivalent to successive subtraction by
powers of the base.
• Let’s use the division remainder method to again
convert 190 in decimal to base 3.
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2.3 Converting Between Bases
• Converting 190 to base 3...
– First we take the number
that we wish to convert and
divide it by the radix in
which we want to express
our result.
– In this case, 3 divides 190
63 times, with a remainder
of 1.
– Record the quotient and the
remainder.
15
2.3 Converting Between Bases
• Converting 190 to base 3...
– 63 is evenly divisible by 3.
– Our remainder is zero, and
the quotient is 21.
16
2.3 Converting Between Bases
• Converting 190 to base 3...
– Continue in this way until
the quotient is zero.
– In the final calculation, we
note that 3 divides 2 zero
times with a remainder of 2.
– Our result, reading from
bottom to top is:
19010 = 210013
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2.3 Converting Between Bases
• Fractional values can be approximated in all
base systems.
• Unlike integer values, fractions do not
necessarily have exact representations under all
radices.
• The quantity ½ is exactly representable in the
binary and decimal systems, but is not in the
ternary (base 3) numbering system.
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2.3 Converting Between Bases
• Fractional decimal values have nonzero digits to
the right of the decimal point.
• Fractional values of other radix systems have
nonzero digits to the right of the radix point.
• Numerals to the right of a radix point represent
negative powers of the radix:
0.4710 = 4  10 -1 + 7  10 -2
0.112 = 1  2 -1 + 1  2 -2
= ½ + ¼
= 0.5 + 0.25 = 0.75
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2.3 Converting Between Bases
• As with whole-number conversions, you can use
either of two methods: a subtraction method or an
easy multiplication method.
• The subtraction method for fractions is identical to
the subtraction method for whole numbers.
Instead of subtracting positive powers of the target
radix, we subtract negative powers of the radix.
• We always start with the largest value first, n -1,
where n is our radix, and work our way along
using larger negative exponents.
20
2.3 Converting Between Bases
• The calculation to the
right is an example of
using the subtraction
method to convert the
decimal 0.8125 to
binary.
– Our result, reading from
top to bottom is:
0.812510 = 0.11012
– Of course, this method
works with any base,
not just binary.
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2.3 Converting Between Bases
• Using the multiplication
method to convert the
decimal 0.8125 to binary,
we multiply by the radix 2.
– The first product carries
into the units place.
22
2.3 Converting Between Bases
• Converting 0.8125 to
binary . . .
– Ignoring the value in
the units place at each
step, continue
multiplying each
fractional part by the
radix.
23
2.3 Converting Between Bases
• Converting 0.8125 to binary . . .
– You are finished when the
product is zero, or until you
have reached the desired
number of binary places.
– Our result, reading from top to
bottom is:
0.812510 = 0.11012
– This method also works with
any base. Just use the target
radix as the multiplier.
24
2.3 Converting Between Bases
• The binary numbering system is the most
important radix system for digital computers.
• However, it is difficult to read long strings of binary
numbers -- and even a modestly-sized decimal
number becomes a very long binary number.
– For example: 110101000110112 = 1359510
• For compactness and ease of reading, binary
values are usually expressed using the
hexadecimal, or base-16, numbering system.
25
2.3 Converting Between Bases
• The hexadecimal numbering system uses the
numerals 0 through 9 and the letters A through F.
– The decimal number 12 is C16.
– The decimal number 26 is 1A16.
• It is easy to convert between base 16 and base 2,
because 16 = 24.
• Thus, to convert from binary to hexadecimal, all
we need to do is group the binary digits into
groups of four.
A group of four binary digits is called a hextet
26
2.3 Converting Between Bases
• Using groups of hextets, the binary number
110101000110112 (= 1359510) in hexadecimal is:
If the number of bits is not a
multiple of 4, pad on the left
with zeros.
• Octal (base 8) values are derived from binary by
using groups of three bits (8 = 23):
Octal was very useful when computers used six-bit words.
27
2.4 Signed Integer Representation
• The conversions we have so far presented have
involved only unsigned numbers.
• To represent signed integers, computer systems
allocate the high-order bit to indicate the sign of a
number.
– The high-order bit is the leftmost bit. It is also called the
most significant bit.
– 0 is used to indicate a positive number; 1 indicates a
negative number.
• The remaining bits contain the value of the number
(but this can be interpreted different ways)
28
2.4 Signed Integer Representation
• There are three ways in which signed binary
integers may be expressed:
– Signed magnitude
– One’s complement
– Two’s complement
• In an 8-bit word, signed magnitude
representation places the absolute value of
the number in the 7 bits to the right of the
sign bit.
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2.4 Signed Integer Representation
• For example, in 8-bit signed magnitude
representation:
+3 is:
- 3 is:
00000011
10000011
• Computers perform arithmetic operations on
signed magnitude numbers in much the same
way as humans carry out pencil and paper
arithmetic.
– Humans often ignore the signs of the operands
while performing a calculation, applying the
appropriate sign after the calculation is complete.
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2.4 Signed Integer Representation
• Binary addition is as easy as it gets. You need
to know only four rules:
0 + 0 =
1 + 0 =
0
1
0 + 1 = 1
1 + 1 = 10
• The simplicity of this system makes it possible
for digital circuits to carry out arithmetic
operations.
– We will describe these circuits in Chapter 3.
Let’s see how the addition rules work with signed
magnitude numbers . . .
31
2.4 Signed Integer Representation
• Example:
– Using signed magnitude
binary arithmetic, find the
sum of 75 and 46.
• First, convert 75 and 46 to
binary, and arrange as a sum,
but separate the (positive)
sign bits from the magnitude
bits.
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2.4 Signed Integer Representation
• Example:
– Using signed magnitude
binary arithmetic, find the
sum of 75 and 46.
• Just as in decimal arithmetic,
we find the sum starting with
the rightmost bit and work left.
33
2.4 Signed Integer Representation
• Example:
– Using signed magnitude
binary arithmetic, find the
sum of 75 and 46.
• In the second bit, we have a
carry, so we note it above the
third bit.
34
2.4 Signed Integer Representation
• Example:
– Using signed magnitude
binary arithmetic, find the
sum of 75 and 46.
• The third and fourth bits also
give us carries.
35
2.4 Signed Integer Representation
• Example:
– Using signed magnitude binary
arithmetic, find the sum of 75
and 46.
• Once we have worked our way
through all eight bits, we are
done.
In this example, we were careful to pick two values whose
sum would fit into seven bits. If that is not the case, we
have a problem.
36
2.4 Signed Integer Representation
• Example:
– Using signed magnitude binary
arithmetic, find the sum of 107
and 46.
• We see that the carry from the
seventh bit overflows and is
discarded, giving us the
erroneous result: 107 + 46 = 25.
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2.4 Signed Integer Representation
• The signs in signed
magnitude representation
work just like the signs in
pencil and paper arithmetic.
– Example: Using signed
magnitude binary arithmetic,
find the sum of - 46 and - 25.
• Because the signs are the same, all we do is
add the numbers and supply the negative sign
when we are done.
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2.4 Signed Integer Representation
• Mixed sign addition (or
subtraction) is done the
same way.
– Example: Using signed
magnitude binary arithmetic,
find the sum of 46 and - 25.
• The sign of the result gets the sign of the number
that is larger.
– Note the “borrows” from the second and sixth bits.
39
2.4 Signed Integer Representation
• Signed magnitude representation is easy for
people to understand, but it requires
complicated computer hardware.
• Another disadvantage of signed magnitude is
that it allows two different representations for
zero: positive zero and negative zero.
• For these reasons (among others) computers
systems employ complement systems for
numeric value representation.
40
2.4 Signed Integer Representation
• In complement systems, negative values are
represented by some difference between a
number and its base.
• The diminished radix complement of a non-zero
number N in base r with d digits is (rd – 1) – N
• In the binary system, this gives us one’s
complement. It amounts to little more than flipping
the bits of a binary number.
41
2.4 Signed Integer Representation
• For example, using 8-bit one’s complement
representation:
+ 3 is:
- 3 is:
00000011
11111100
• In one’s complement representation, as with
signed magnitude, negative values are
indicated by a 1 in the high order bit.
• Complement systems are useful because they
eliminate the need for subtraction. The
difference of two values is found by adding the
minuend to the complement of the subtrahend.
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2.4 Signed Integer Representation
• With one’s complement
addition, the carry bit is
“carried around” and added
to the sum.
– Example: Using one’s
complement binary arithmetic,
find the sum of 48 and - 19
We note that 19 in binary is
so -19 in one’s complement is:
43
00010011,
11101100.
2.4 Signed Integer Representation
• Although the “end carry around” adds some
complexity, one’s complement is simpler to
implement than signed magnitude.
• But it still has the disadvantage of having two
different representations for zero: positive zero
and negative zero.
• Two’s complement solves this problem.
• Two’s complement is the radix complement of the
binary numbering system; the radix complement
of a non-zero number N in base r with d digits is
rd – N.
44
2.4 Signed Integer Representation
• To express a value in two’s complement
representation:
– If the number is positive, just convert it to binary and
you’re done.
– If the number is negative, find the one’s complement of
the number and then add 1.
• Example:
– In 8-bit binary, 3 is:
00000011
– -3 using one’s complement representation is:
11111100
– Adding 1 gives us -3 in two’s complement form:
11111101.
45
2.4 Signed Integer Representation
• With two’s complement arithmetic, all we do is add
our two binary numbers. Just discard any carries
emitting from the high order bit.
– Example: Using one’s
complement binary
arithmetic, find the sum of
48 and - 19.
We note that 19 in binary is:
00010011,
so -19 using one’s complement is: 11101100,
and -19 using two’s complement is: 11101101.
46
2.4 Signed Integer Representation
• Excess-M representation (also called offset binary
representation) is another way for unsigned binary
values to represent signed integers.
– Excess-M representation is intuitive because the binary
string with all 0s represents the smallest number, whereas
the binary string with all 1s represents the largest value.
• An unsigned binary integer M (called the bias)
represents the value 0, whereas all zeroes in the bit
pattern represents the integer 2M.
• The integer is interpreted as positive or negative
depending on where it falls in the range.
47
2.4 Signed Integer Representation
• If n bits are used for the binary representation, we
select the bias in such a manner that we split the
range equally.
• Typically we choose a bias of 2n-1 - 1.
– For example, if we were using 4-bit representation, the
bias should be 24 -1 - 1 = 7.
• Just as with signed magnitude, one’s complement,
and two’s complement, there is a specific range of
values that can be expressed in n bits.
48
2.4 Signed Integer Representation
• The unsigned binary value for a signed integer using
excess-M representation is determined simply by
adding M to that integer.
– For example, assuming that we are using excess-7
representation, the integer 010 is represented as 0 - 7 = 710
= 01112.
– The integer 310 is represented as 3 + 7 = 10 10 = 10102.
– The integer -7 is represented as -7 + 7 = 0 10 = 00002 .
– To find the decimal value of the excess-7 binary number
11112 subtract 7: 11112 = 1510 and 15 - 7 = 8; thus 11112,
in excess-7 is +810.
49
2.4 Signed Integer Representation
• Lets compare our representations:
50
2.4 Signed Integer Representation
• When we use any finite number of bits to
represent a number, we always run the risk of
the result of our calculations becoming too large
or too small to be stored in the computer.
• While we can’t always prevent overflow, we can
always detect overflow.
• In complement arithmetic, an overflow condition
is easy to detect.
51
2.4 Signed Integer Representation
• Example:
– Using two’s complement binary
arithmetic, find the sum of 107
and 46.
• We see that the nonzero carry
from the seventh bit overflows into
the sign bit, giving us the
erroneous result: 107 + 46 = -103.
But overflow into the sign bit does not
always mean that we have an error.
52
2.4 Signed Integer Representation
• Example:
– Using two’s complement binary
arithmetic, find the sum of 23 and
-9.
– We see that there is carry into the
sign bit and carry out. The final
result is correct: 23 + (-9) = 14.
Rule for detecting signed two’s complement overflow: When
the “carry in” and the “carry out” of the sign bit differ,
overflow has occurred. If the carry into the sign bit equals the
carry out of the sign bit, no overflow has occurred.
53
2.4 Signed Integer Representation
• Signed and unsigned numbers are both useful.
– For example, memory addresses are always unsigned.
• Using the same number of bits, unsigned integers
can express twice as many “positive” values as
signed numbers.
• Trouble arises if an unsigned value “wraps around.”
– In four bits: 1111 + 1 = 0000.
• Good programmers stay alert for this kind of
problem.
54
2.4 Signed Integer Representation
• Research into finding better arithmetic algorithms
has continued for over 50 years.
• One of the many interesting products of this work
is Booth’s algorithm.
• In most cases, Booth’s algorithm carries out
multiplication faster and more accurately than
pencil-and-paper methods.
• The general idea is to replace arithmetic
operations with bit shifting to the extent possible.
55
2.4 Signed Integer Representation
In Booth’s algorithm:
• If the current multiplier bit is
1 and the preceding bit was
0, subtract the multiplicand
from the product
• If the current multiplier bit is
0 and the preceding bit was
1, we add the multiplicand to
the product
• If we have a 00 or 11 pair,
we simply shift.
• Assume a mythical “0”
starting bit
• Shift after each step
56
0011
x 0110
+ 0000
- 0011
+ 0000
+ 0011
(shift)
(subtract)
(shift)
(add)
00010010
We see that 3  6 = 18!
.
2.4 Signed Integer Representation
• Here is a larger
example.
Ignore all bits over 2n.
00110101
x
01111110
+ 0000000000000000
+ 111111111001011
+ 00000000000000
+ 0000000000000
+ 000000000000
+ 00000000000
+ 0000000000
+ 000110101_______
10001101000010110
53  126 = 6678!
57
2.4 Signed Integer Representation
• Overflow and carry are tricky ideas.
• Signed number overflow means nothing in the
context of unsigned numbers, which set a carry
flag instead of an overflow flag.
• If a carry out of the leftmost bit occurs with an
unsigned number, overflow has occurred.
• Carry and overflow occur independently of each
other.
The table on the next slide summarizes these ideas.
58
2.4 Signed Integer Representation
59
2.4 Signed Integer Representation
• We can do binary multiplication and division by 2
very easily using an arithmetic shift operation
• A left arithmetic shift inserts a 0 in for the
rightmost bit and shifts everything else left one
bit; in effect, it multiplies by 2
• A right arithmetic shift shifts everything one bit to
the right, but copies the sign bit; it divides by 2
• Let’s look at some examples.
60
2.4 Signed Integer Representation
Example:
Multiply the value 11 (expressed using 8-bit signed two’s
complement representation) by 2.
We start with the binary value for 11:
00001011 (+11)
We shift left one place, resulting in:
00010110 (+22)
The sign bit has not changed, so the value is valid.
To multiply 11 by 4, we simply perform a left shift twice.
61
2.4 Signed Integer Representation
Example:
Divide the value 12 (expressed using 8-bit signed two’s
complement representation) by 2.
We start with the binary value for 12:
00001100 (+12)
We shift left one place, resulting in:
00000110 (+6)
(Remember, we carry the sign bit to the left as we shift.)
To divide 12 by 4, we right shift twice.
62
2.5 Floating-Point Representation
• The signed magnitude, one’s complement,
and two’s complement representation that we
have just presented deal with signed integer
values only.
• Without modification, these formats are not
useful in scientific or business applications
that deal with real number values.
• Floating-point representation solves this
problem.
63
2.5 Floating-Point Representation
• If we are clever programmers, we can perform
floating-point calculations using any integer format.
• This is called floating-point emulation, because
floating point values aren’t stored as such; we just
create programs that make it seem as if floatingpoint values are being used.
• Most of today’s computers are equipped with
specialized hardware that performs floating-point
arithmetic with no special programming required.
64
2.5 Floating-Point Representation
• Floating-point numbers allow an arbitrary
number of decimal places to the right of the
decimal point.
– For example: 0.5  0.25 = 0.125
• They are often expressed in scientific notation.
– For example:
0.125 = 1.25  10-1
5,000,000 = 5.0  106
65
2.5 Floating-Point Representation
• Computers use a form of scientific notation for
floating-point representation
• Numbers written in scientific notation have three
components:
66
2.5 Floating-Point Representation
• Computer representation of a floating-point
number consists of three fixed-size fields:
• This is the standard arrangement of these fields.
Note: Although “significand” and “mantissa” do not technically mean the same
thing, many people use these terms interchangeably. We use the term “significand”
to refer to the fractional part of a floating point number.
67
2.5 Floating-Point Representation
• The one-bit sign field is the sign of the stored value.
• The size of the exponent field determines the range
of values that can be represented.
• The size of the significand determines the precision
of the representation.
68
2.5 Floating-Point Representation
• We introduce a hypothetical “Simple Model” to
explain the concepts
• In this model:
– A floating-point number is 14 bits in length
– The exponent field is 5 bits
– The significand field is 8 bits
69
2.5 Floating-Point Representation
• The significand is always preceded by an implied
binary point.
• Thus, the significand always contains a fractional
binary value.
• The exponent indicates the power of 2 by which the
significand is multiplied.
70
2.5 Floating-Point Representation
• Example:
– Express 3210 in the simplified 14-bit floating-point
model.
• We know that 32 is 25. So in (binary) scientific
notation 32 = 1.0 x 25 = 0.1 x 26.
– In a moment, we’ll explain why we prefer the second
notation versus the first.
• Using this information, we put 110 (= 610) in the
exponent field and 1 in the significand as shown.
71
2.5 Floating-Point Representation
• The illustrations shown at
the right are all equivalent
representations for 32
using our simplified
model.
• Not only do these
synonymous
representations waste
space, but they can also
cause confusion.
72
2.5 Floating-Point Representation
• Another problem with our system is that we have
made no allowances for negative exponents. We
have no way to express 0.5 (=2 -1)! (Notice that
there is no sign in the exponent field.)
All of these problems can be fixed with no
changes to our basic model.
73
2.5 Floating-Point Representation
• To resolve the problem of synonymous forms,
we establish a rule that the first digit of the
significand must be 1, with no ones to the left of
the radix point.
• This process, called normalization, results in a
unique pattern for each floating-point number.
– In our simple model, all significands must have the
form 0.1xxxxxxxx
– For example, 4.5 = 100.1 x 20 = 1.001 x 22 = 0.1001 x
23. The last expression is correctly normalized.
In our simple instructional model, we use no implied bits.
74
2.5 Floating-Point Representation
• To provide for negative exponents, we will use a
biased exponent.
– In our case, we have a 5-bit exponent.
– 25-1 – 1 = 24-1 = 15
– Thus will use 15 for our bias: our exponent will use
excess-15 representation.
• In our model, exponent values less than 15 are
negative, representing fractional numbers.
75
2.5 Floating-Point Representation
• Example:
– Express 3210 in the revised 14-bit floating-point model.
• We know that 32 = 1.0 x 25 = 0.1 x 26.
• To use our excess 15 biased exponent, we add 15 to
6, giving 2110 (=101012).
• So we have:
76
2.5 Floating-Point Representation
• Example:
– Express 0.062510 in the revised 14-bit floating-point
model.
• We know that 0.0625 is 2-4. So in (binary) scientific
notation 0.0625 = 1.0 x 2-4 = 0.1 x 2 -3.
• To use our excess 15 biased exponent, we add 15 to
-3, giving 1210 (=011002).
77
2.5 Floating-Point Representation
• Example:
– Express -26.62510 in the revised 14-bit floating-point
model.
• We find 26.62510 = 11010.1012. Normalizing, we
have: 26.62510 = 0.11010101 x 2 5.
• To use our excess 15 biased exponent, we add 15 to
5, giving 2010 (=101002). We also need a 1 in the sign
bit.
78
2.5 Floating-Point Representation
• The IEEE has established a standard for
floating-point numbers
• The IEEE-754 single precision floating point
standard uses an 8-bit exponent (with a bias of
127) and a 23-bit significand.
• The IEEE-754 double precision standard uses
an 11-bit exponent (with a bias of 1023) and a
52-bit significand.
79
2.5 Floating-Point Representation
• In both the IEEE single-precision and doubleprecision floating-point standard, the significant has
an implied 1 to the LEFT of the radix point.
– The format for a significand using the IEEE format is:
1.xxx…
– For example, 4.5 = .1001 x 23 in IEEE format is 4.5 =
1.001 x 22. The 1 is implied, which means is does not need
to be listed in the significand (the significand would
include only 001).
80
2.5 Floating-Point Representation
• Example: Express -3.75 as a floating point number
using IEEE single precision.
• First, let’s normalize according to IEEE rules:
– 3.75 = -11.112 = -1.111 x 21
– The bias is 127, so we add 127 + 1 = 128 (this is our
exponent)
– The first 1 in the significand is implied, so we have:
(implied)
– Since we have an implied 1 in the significand, this equates
to
-(1).1112 x 2 (128 – 127) = -1.1112 x 21 = -11.112 = -3.75.
81
2.5 Floating-Point Representation
• Using the IEEE-754 single precision floating point
standard:
– An exponent of 255 indicates a special value.
• If the significand is zero, the value is  infinity.
• If the significand is nonzero, the value is NaN, “not a
number,” often used to flag an error condition.
• Using the double precision standard:
– The “special” exponent value for a double precision number
is 2047, instead of the 255 used by the single precision
standard.
82
2.5 Floating-Point Representation
• Both the 14-bit model that we have presented
and the IEEE-754 floating point standard allow
two representations for zero.
– Zero is indicated by all zeros in the exponent and the
significand, but the sign bit can be either 0 or 1.
• This is why programmers should avoid testing a
floating-point value for equality to zero.
– Negative zero does not equal positive zero.
83
2.5 Floating-Point Representation
• Floating-point addition and subtraction are done
using methods analogous to how we perform
calculations using pencil and paper.
• The first thing that we do is express both
operands in the same exponential power, then
add the numbers, preserving the exponent in the
sum.
• If the exponent requires adjustment, we do so at
the end of the calculation.
84
2.5 Floating-Point Representation
• Example:
– Find the sum of 1210 and 1.2510 using the 14-bit “simple”
floating-point model.
• We find 1210 = 0.1100 x 2 4. And 1.2510 = 0.101 x 2 1 =
0.000101 x 2 4.
• Thus, our sum is
0.110101 x 2 4.
85
2.5 Floating-Point Representation
• Floating-point multiplication is also carried out in
a manner akin to how we perform multiplication
using pencil and paper.
• We multiply the two operands and add their
exponents.
• If the exponent requires adjustment, we do so at
the end of the calculation.
86
2.5 Floating-Point Representation
• Example:
– Find the product of 1210 and 1.2510 using the 14-bit
floating-point model.
• We find 1210 = 0.1100 x 2 4. And 1.2510 = 0.101 x 2 1.
• Thus, our product is
0.0111100 x 2 5 =
0.1111 x 2 4.
• The normalized
product requires an
exponent of 1910 =
100112.
87
2.5 Floating-Point Representation
• No matter how many bits we use in a floating-point
representation, our model must be finite.
• The real number system is, of course, infinite, so our
models can give nothing more than an approximation
of a real value.
• At some point, every model breaks down, introducing
errors into our calculations.
• By using a greater number of bits in our model, we
can reduce these errors, but we can never totally
eliminate them.
88
2.5 Floating-Point Representation
• Our job becomes one of reducing error, or at least
being aware of the possible magnitude of error in
our calculations.
• We must also be aware that errors can compound
through repetitive arithmetic operations.
• For example, our 14-bit model cannot exactly
represent the decimal value 128.5. In binary, it is 9
bits wide:
10000000.12 = 128.510
89
2.5 Floating-Point Representation
• When we try to express 128.510 in our 14-bit model,
we lose the low-order bit, giving a relative error of:
128.5 - 128
 0.39%
128.5
• If we had a procedure that repetitively added 0.5 to
128.5, we would have an error of nearly 2% after only
four iterations.
90
2.5 Floating-Point Representation
• Floating-point errors can be reduced when we use
operands that are similar in magnitude.
• If we were repetitively adding 0.5 to 128.5, it
would have been better to iteratively add 0.5 to
itself and then add 128.5 to this sum.
• In this example, the error was caused by loss of
the low-order bit.
• Loss of the high-order bit is more problematic.
91
2.5 Floating-Point Representation
• Floating-point overflow and underflow can cause
programs to crash.
• Overflow occurs when there is no room to store
the high-order bits resulting from a calculation.
• Underflow occurs when a value is too small to
store, possibly resulting in division by zero.
Experienced programmers know that it’s better for a
program to crash than to have it produce incorrect, but
plausible, results.
92
2.5 Floating-Point Representation
• When discussing floating-point numbers, it is
important to understand the terms range,
precision, and accuracy.
• The range of a numeric integer format is the
difference between the largest and smallest
values that can be expressed.
• Accuracy refers to how closely a numeric
representation approximates a true value.
• The precision of a number indicates how much
information we have about a value
93
2.5 Floating-Point Representation
• Most of the time, greater precision leads to better
accuracy, but this is not always true.
– For example, 3.1333 is a value of pi that is accurate to
two digits, but has 5 digits of precision.
• There are other problems with floating point
numbers.
• Because of truncated bits, you cannot always
assume that a particular floating point operation is
commutative or distributive.
94
2.5 Floating-Point Representation
• This means that we cannot assume:
(a + b) + c = a + (b + c) or
a*(b + c) = ab + ac
• Moreover, to test a floating point value for equality to
some other number, it is best to declare a “nearness to x”
epsilon value. For example, instead of checking to see if
floating point x is equal to 2 as follows:
if x = 2 then …
it is better to use:
if (abs(x - 2) < epsilon) then ...
(assuming we have epsilon defined correctly!)
95
2.6 Character Codes
• Calculations aren’t useful until their results can
be displayed in a manner that is meaningful to
people.
• We also need to store the results of calculations,
and provide a means for data input.
• Thus, human-understandable characters must be
converted to computer-understandable bit
patterns using some sort of character encoding
scheme.
96
2.6 Character Codes
• As computers have evolved, character codes
have evolved.
• Larger computer memories and storage
devices permit richer character codes.
• The earliest computer coding systems used six
bits.
• Binary-coded decimal (BCD) was one of these
early codes. It was used by IBM mainframes in
the 1950s and 1960s.
97
2.6 Character Codes
• In 1964, BCD was extended to an 8-bit code,
Extended Binary-Coded Decimal Interchange
Code (EBCDIC).
• EBCDIC was one of the first widely-used
computer codes that supported upper and
lowercase alphabetic characters, in addition to
special characters, such as punctuation and
control characters.
• EBCDIC and BCD are still in use by IBM
mainframes today.
98
2.6 Character Codes
• Other computer manufacturers chose the 7-bit
ASCII (American Standard Code for Information
Interchange) as a replacement for 6-bit codes.
• While BCD and EBCDIC were based upon
punched card codes, ASCII was based upon
telecommunications (Telex) codes.
• Until recently, ASCII was the dominant
character code outside the IBM mainframe
world.
99
2.6 Character Codes
• Many of today’s systems embrace Unicode, a 16bit system that can encode the characters of
every language in the world.
– The Java programming language, and some operating
systems now use Unicode as their default character
code.
• The Unicode codespace is divided into six parts.
The first part is for Western alphabet codes,
including English, Greek, and Russian.
100
2.6 Character Codes
• The Unicode codespace allocation is
shown at the right.
• The lowest-numbered
Unicode characters
comprise the ASCII
code.
• The highest provide for
user-defined codes.
101
2.8 Error Detection and Correction
• It is physically impossible for any data recording or
transmission medium to be 100% perfect 100% of
the time over its entire expected useful life.
• As more bits are packed onto a square centimeter
of disk storage, as communications transmission
speeds increase, the likelihood of error increases-sometimes geometrically.
• Thus, error detection and correction is critical to
accurate data transmission, storage and retrieval.
102
2.8 Error Detection and Correction
• Check digits, appended to the end of a long number,
can provide some protection against data input
errors.
– The last characters of UPC barcodes and ISBNs are check
digits.
• Longer data streams require more economical and
sophisticated error detection mechanisms.
• Cyclic redundancy checking (CRC) codes provide
error detection for large blocks of data.
103
2.8 Error Detection and Correction
• Checksums and CRCs are examples of systematic
error detection.
• In systematic error detection a group of error control
bits is appended to the end of the block of
transmitted data.
– This group of bits is called a syndrome.
• CRCs are polynomials over the modulo 2 arithmetic
field.
The mathematical theory behind modulo 2 polynomials is
beyond our scope. However, we can easily work with it
without knowing its theoretical underpinnings.
104
2.8 Error Detection and Correction
• Modulo 2 arithmetic works like clock arithmetic.
• In clock arithmetic, if we add 2 hours to 11:00, we
get 1:00.
• In modulo 2 arithmetic if we add 1 to 1, we get 0.
The addition rules couldn’t be simpler:
0+0=0
1+0=1
0+1=1
1+1=0
You will fully understand why modulo 2 arithmetic is so
handy after you study digital circuits in Chapter 3.
105
2.8 Error Detection and Correction
• Find the quotient and
remainder when 1111101 is
divided by 1101 in modulo 2
arithmetic.
– As with traditional division,
we note that the dividend is
divisible once by the divisor.
– We place the divisor under the
dividend and perform modulo
2 subtraction.
106
2.8 Error Detection and Correction
• Find the quotient and
remainder when 1111101 is
divided by 1101 in modulo 2
arithmetic…
– Now we bring down the next
bit of the dividend.
– We see that 00101 is not
divisible by 1101. So we place
a zero in the quotient.
107
2.8 Error Detection and Correction
• Find the quotient and
remainder when 1111101 is
divided by 1101 in modulo 2
arithmetic…
– 1010 is divisible by 1101 in
modulo 2.
– We perform the modulo 2
subtraction.
108
2.8 Error Detection and Correction
• Find the quotient and
remainder when 1111101 is
divided by 1101 in modulo 2
arithmetic…
– We find the quotient is 1011,
and the remainder is 0010.
• This procedure is very useful
to us in calculating CRC
syndromes.
Note: The divisor in this example corresponds
to a modulo 2 polynomial: X 3 + X 2 + 1.
109
2.8 Error Detection and Correction
• Suppose we want to transmit the
information string: 1111101.
• The receiver and sender decide to
use the (arbitrary) polynomial
pattern, 1101.
• The information string is shifted
left by one position less than the
number of positions in the divisor.
• The remainder is found through
modulo 2 division (at right) and
added to the information string:
1111101000 + 111 = 1111101111.
110
2.8 Error Detection and Correction
• If no bits are lost or
corrupted, dividing the
received information string
by the agreed upon pattern
will give a remainder of zero.
• We see this is so in the
calculation at the right.
• Real applications use longer
polynomials to cover larger
information strings.
– Some of the standard polynomials are listed in the text.
111
2.8 Error Detection and Correction
• Data transmission errors are easy to fix once an error
is detected.
– Just ask the sender to transmit the data again.
• In computer memory and data storage, however, this
cannot be done.
– Too often the only copy of something important is in
memory or on disk.
• Thus, to provide data integrity over the long term,
error correcting codes are required.
112
2.8 Error Detection and Correction
• Hamming codes and Reed-Solomon codes are two
important error correcting codes.
• Reed-Solomon codes are particularly useful in
correcting burst errors that occur when a series of
adjacent bits are damaged.
– Because CD-ROMs are easily scratched, they employ a type
of Reed-Solomon error correction.
• Because the mathematics of Hamming codes is
much simpler than Reed-Solomon, we discuss
Hamming codes in detail.
113
2.8 Error Detection and Correction
• Hamming codes are code words formed by adding
redundant check bits, or parity bits, to a data word.
• The Hamming distance between two code words is
the number of bits in which two code words differ.
This pair of bytes has a
Hamming distance of 3:
• The minimum Hamming distance for a code is the
smallest Hamming distance between all pairs of
words in the code.
114
2.8 Error Detection and Correction
• The minimum Hamming distance for a code,
D(min), determines its error detecting and error
correcting capability.
• For any code word, X, to be interpreted as a
different valid code word, Y, at least D(min)
single-bit errors must occur in X.
• Thus, to detect k (or fewer) single-bit errors, the
code must have a Hamming distance of
D(min) = k + 1.
115
2.8 Error Detection and Correction
• Hamming codes can detect D(min) - 1 errors
and correct
errors
• Thus, a Hamming distance of 2k + 1 is
required to be able to correct k errors in any
data word.
• Hamming distance is provided by adding a
suitable number of parity bits to a data word.
116
2.8 Error Detection and Correction
• Suppose we have a set of n-bit code words
consisting of m data bits and r (redundant) parity
bits.
• Suppose also that we wish to detect and correct
one single bit error only.
• An error could occur in any of the n bits, so each
code word can be associated with n invalid code
words at a Hamming distance of 1.
• Therefore, we have n + 1 bit patterns for each
code word: one valid code word, and n invalid
code words
117
2.8 Error Detection and Correction
• Using n bits, we have 2 n possible bit patterns. We
have 2 m valid code words with r check bits (where
n = m + r).
• For each valid codeword, we have (n+1) bit
patterns (1 legal and N illegal).
• This gives us the inequality:
(n + 1)  2 m  2 n
• Because n = m + r, we can rewrite the inequality
as:
(m + r + 1)  2 m  2 m + r or (m + r + 1)  2 r
– This inequality gives us a lower limit on the number of
check bits that we need in our code words.
118
2.8 Error Detection and Correction
• Suppose we have data words of length m = 4.
Then:
(4 + r + 1)  2 r
implies that r must be greater than or equal to 3.
– We should always use the smallest value of r that makes
the inequality true.
• This means to build a code with 4-bit data words
that will correct single-bit errors, we must add 3
check bits.
• Finding the number of check bits is the hard part.
The rest is easy.
119
2.8 Error Detection and Correction
• Suppose we have data words of length m = 8.
Then:
(8 + r + 1)  2 r
implies that r must be greater than or equal to 4.
• This means to build a code with 8-bit data words
that will correct single-bit errors, we must add 4
check bits, creating code words of length 12.
• So how do we assign values to these check
bits?
120
2.8 Error Detection and Correction
• With code words of length 12, we observe that each
of the bits, numbered 1 though 12, can be expressed
in powers of 2. Thus:
1 = 20
5 = 22 + 2 0
9 = 23 + 2 0
2 = 21
6 = 22 + 2 1
10 = 2 3 + 2 1
3 = 21+ 2 0 7 = 22 + 21 + 2 0
11 = 2 3 + 2 1 + 2 0
4 = 22
8 = 23
12 = 2 3 + 2 2
– 1 (= 20) contributes to all of the odd-numbered digits.
– 2 (= 21) contributes to the digits, 2, 3, 6, 7, 10, and 11.
– . . . And so forth . . .
• We can use this idea in the creation of our check bits.
121
2.8 Error Detection and Correction
• Using our code words of length 12, number each
bit position starting with 1 in the low-order bit.
• Each bit position corresponding to a power of 2
will be occupied by a check bit.
• These check bits contain the parity of each bit
position for which it participates in the sum.
122
2.8 Error Detection and Correction
• Since 1 (=20) contributes to the values 1, 3 , 5, 7, 9,
and 11, bit 1 will check parity over bits in these
positions.
• Since 2 (= 21) contributes to the values 2, 3, 6, 7,
10, and 11, bit 2 will check parity over these bits.
• For the word 11010110, assuming even parity, we
have a value of 1 for check bit 1, and a value of 0 for
check bit 2.
What are the values for the other parity bits?
123
2.8 Error Detection and Correction
• The completed code word is shown above.
–
–
–
Bit 1checks the bits 3, 5, 7, 9, and 11, so its value is 1 to
ensure even parity within this group.
Bit 4 checks the bits 5, 6, 7, and 12, so its value is 1.
Bit 8 checks the bits 9, 10, 11, and 12, so its value is
also 1.
• Using the Hamming algorithm, we can not only
detect single bit errors in this code word, but also
correct them!
124
2.8 Error Detection and Correction
• Suppose an error occurs in bit 5, as shown above.
Our parity bit values are:
–
–
–
–
125
Bit 1 checks 1, 3, 5, 7, 9, and 11. This is incorrect as we
have a total of 3 ones (which is not even parity).
Bit 2 checks bits 2, 3, 6, 7, 10, and 11. The parity is correct.
Bit 4 checks bits 4, 5, 6, 7, and 12. This parity is incorrect,
as we 3 ones.
Bit 8 checks bit 8, 9, 10, 11, and 12. This parity is correct.
2.8 Error Detection and Correction
• We have erroneous parity for check bits 1 and 4.
• With two parity bits that don’t check, we know that
the error is in the data, and not in a parity bit.
• Which data bits are in error? We find out by
adding the bit positions of the erroneous bits.
• Simply, 1 + 4 = 5. This tells us that the error is in
bit 5. If we change bit 5 to a 1, all parity bits check
and our data is restored.
126
Chapter 2 Conclusion
• Computers store data in the form of bits, bytes,
and words using the binary numbering system.
• Hexadecimal numbers are formed using four-bit
groups called nibbles.
• Signed integers can be stored in one’s
complement, two’s complement, or signed
magnitude representation.
• Floating-point numbers are usually coded using
the IEEE 754 floating-point standard.
127
Chapter 2 Conclusion
• Floating-point operations are not necessarily
commutative or distributive.
• Character data is stored using ASCII, EBCDIC,
or Unicode.
• Error detecting and correcting codes are
necessary because we can expect no
transmission or storage medium to be perfect.
• CRC, Reed-Solomon, and Hamming codes are
three important error control codes.
128